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相关概念视频

Multiple Comparison Tests01:13

Multiple Comparison Tests

4.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.4K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.3K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

522
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
522
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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相关实验视频

Updated: Jan 14, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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确定多个测试的最佳顺序.

Lucas Böttcher1,2, Stefan Felder3,4

  • 1Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, Germany.

Statistics in medicine
|October 17, 2025
PubMed
概括
此摘要是机器生成的。

优化医疗测试选择包括平衡好处和危害. 本研究介绍了一种方法来计算单个和多个诊断测试的增量净益 (INB),指导最佳测试选择和序列.

关键词:
组合测试 组合测试 组合测试诊断测试 诊断测试 测试 诊断测试最优的测试最优的测试接收器的运行特性.测试的临界值.处理值的治疗值.信息信息的价值信息的价值.

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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
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Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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科学领域:

  • 医疗决策的制定 医疗决策的制定
  • 卫生经济学 卫生经济学
  • 诊断试验评价 诊断试验评价

背景情况:

  • 临床决策涉及平衡治疗的好处与测试的危害和成本.
  • 准确的诊断对于有效的患者管理和资源分配至关重要.

研究的目的:

  • 量化单个和多个诊断测试的增量净益 (INB).
  • 根据疾病概率和成本效益权衡,开发一个优化测试选择和测试序列的框架.

主要方法:

  • 将INB分解为信息价值和与测试相关的成本/损害.
  • 对测试组合的聚合函数 (连接函数,分割函数,多数函数) 的分析.
  • 适用于前列腺癌,结肠直肠癌和冠状动脉疾病诊断.

主要成果:

  • 最佳的测试选择和测试顺序取决于测试前的疾病概率和治疗成本效益比.
  • 决策边界确定了单一,连接,分离和多数测试方法的最佳策略.
  • 来自三个疾病实例的实证数据展示了INB框架的实际应用.

结论:

  • 拟议的INB框架提供了一种全面的方法来优化诊断测试策略.
  • 诊断测试的最佳使用取决于上下文,因疾病的流行率和治疗价值而有所不同.
  • 有一个在线工具可用于可视化INB的组合测试,帮助临床决策支持.